1. Introduction to Logical Data Modeling

• • Importance of logical data modeling in requirements
• • When to use logical data models
• • Relationship between logical and physical data model
• • Elements of a logical data model
• • Read a high-level data model
• • Data model prerequisites
• • Data model sources of information
• • Developing a logical data model

2. Project Context and Drivers

• • Importance of well-defined solution scope
• • Functional decomposition diagram
• • Context-level data flow diagram
• • Sources of requirements
  • • Functional decomposition diagrams
  • • Data flow diagrams
  • • Use case models
  • • Workflow models
  • • Business rules
  • • State diagrams
  • • Class diagrams
  • • Other documentation
• • Types of modeling projects
  • • Transactional business systems
  • • Business intelligence and data warehousing systems
  • • Integration and consolidation of existing systems
  • • Maintenance of existing systems
  • • Enterprise analysis
  • • Commercial off-the-shelf application

3. Conceptual Data Modeling

• • Discovering entities
• • Defining entities
• • Documenting an entity
• • Identifying attributes
• • Distinguishing between entities and attributes

4. Conceptual Data Modeling-Identifying Relationships and Business Rules

• • Model fundamental relationships
• • Cardinality of relationships
  • • One-to-one
  • • One-to-many
  • • Many-to-many
• • Is the relationship mandatory or optional?
• • Naming the relationships

5. Identifying Attributes

• • Discover attributes for the subject area
• • Assign attributes to the appropriate entity
• • Name attributes using established naming conventions
• • Documenting attributes

6. Advanced Relationships

• • Modeling many-to-many relationships
• • Model multiple relationships between the same two entities
• • Model self-referencing relationships
• • Model ternary relationships
• • Identify redundant relationships

7. Completing the Logical Data Model

• • Use supertypes and subtypes to manage complexity
• • Use supertypes and subtypes to represent rules and constraints

8. Data Integrity Through Normalization

• • Normalize a logical data model
  • • First normal form
  • • Second normal form
  • • Third normal form
• • Reasons for denormalization
• • Transactional vs. business intelligence applications

9. Verification and Validation

• • Verify the technical accuracy of a logical data model
• • Use CASE tools to assist in verification
• • Verify the logical data model using other models
  • • Data flow diagram
  • • CRUD matrix